A data-driven statistical model for predicting the critical temperature of a superconductor

We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on therm...

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Bibliographic Details
Published inComputational materials science Vol. 154; pp. 346 - 354
Main Author Hamidieh, Kam
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2018
Subjects
Critical temperature
Data mining
Machine learning
Statistical learning
Superconductivity
Superconductor
Superconductor
Critical temperature
Data mining
Statistical learning
Superconductivity
Machine learning
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Summary:We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model’s predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not; it only gives predictions for superconductors.
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2018.07.052